TL;DR
JobBERT-V3 is a multilingual contrastive learning model that improves cross-lingual job title matching across four languages, outperforming baselines and enabling broader labor market applications.
Contribution
It extends monolingual JobBERT-V2 to support four languages using synthetic translations and a large multilingual dataset, without task-specific supervision.
Findings
Outperforms strong multilingual baselines on TalentCLEF 2025
Achieves consistent performance in monolingual and cross-lingual tasks
Can rank relevant skills for job titles in multiple languages
Abstract
We introduce JobBERT-V3, a contrastive learning-based model for cross-lingual job title matching. Building on the state-of-the-art monolingual JobBERT-V2, our approach extends support to English, German, Spanish, and Chinese by leveraging synthetic translations and a balanced multilingual dataset of over 21 million job titles. The model retains the efficiency-focused architecture of its predecessor while enabling robust alignment across languages without requiring task-specific supervision. Extensive evaluations on the TalentCLEF 2025 benchmark demonstrate that JobBERT-V3 outperforms strong multilingual baselines and achieves consistent performance across both monolingual and cross-lingual settings. While not the primary focus, we also show that the model can be effectively used to rank relevant skills for a given job title, demonstrating its broader applicability in multilingual labor…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
